Vertex construction. Firstly, we use the Simple Linear Iterative Clustering (SLIC) [1]} method to over-segment one image and divide it into a superpixel set, denoted as \(\lbrace s{{p}_{i}}\rbrace _{i=1}^{N}\) , where \(N\)  is the number of superpixels contained in one image. In our experiments, one image is over-segment into about 1000 superpixels. Then we use the conv5-3 layer of the clean model \(\mathcal {C}\)  to extract the high-level semantic features from the whole image and integrate over superpixels. Since the feature maps are downsampled by the operation of several convolutional layers, we apply a bilinear interpolation on the feature maps to obtain the dense feature maps with the same size as the original images, and then an average pooling is performed on a superpixel along the channels. Finally, a 512-dimensional CNN feature vector for each superpixel \(s{{p}_{i}}\)  is obtained.
